Peisong Han is an Assistant Professor in the Department of Biostatistics. He received his PhD in Biostatistics from the University of Michigan in 2013. Before joining the University of Michigan in 2018, Dr. Han was an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo in Canada from 2013 to 2017. His primary research interests include (i) missing data problems in public health studies and survey sampling, and (ii) data integration, especially when summary information is available for some studies and individual-level data is available for others.
PhD, Biostatistics, University of Michigan, 2013
M.S., Statistics, Michigan State University, 2008
B.S., Mathematics, University of Science and Technology of China, 2006
Research Interests & Projects
Missing data problems; Data integration; Biased sampling problems; Case-control studies; Survey sampling; Empirical likelihood; Longitudinal(correlated/clustered) data analysis; Estimating functions
- Han, P., Kong, L., Zhao, J., and Zhou, X. (2019) A General Framework for Quantile Estimation with Incomplete Data. Journal of the Royal Statistical Society - Series B. 81, 305-333.
- Han, P., and Lawless, J. F. (2019) Empirical Likelihood Estimation Using Auxiliary Summary Information with Different Covariate Distributions. Statistica Sinica. 29, 1321-1342.
- Han, P. (2018). A Further Study of Propensity Score Calibration in Missing Data Analysis. Statistica Sinica, 28, 1307-1332.
- Han, P. (2018). Calibration and Multiple Robustness When Data Are Missing Not At Random. Statistica Sinica, 28, 1725-1740.
- Han, P. (2016). Intrinsic Efficiency and Multiple Robustness in Longitudinal Studies with Dropout. Biometrika. 103, 683-700.
- Han, P. (2016). Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation. Scandinavian Journal of Statistics, 43, 246-260.
- Han, P., Song, P. and Wang, L. (2015). Achieving Semiparametric Efficiency Bound in Longitudinal Data Analysis with Dropouts. Journal of Multivariate Analysis, 135, 59-70.
- Han, P. (2014). Multiply Robust Estimation in Regression Analysis with Missing Data. Journal of the American Statistical Association, 109, 1159-1173.
- Han, P. (2014). A Further Study of the Multiply Robust Estimator in Missing Data Analysis. Journal of Statistical Planning and Inference, 148, 101-110.
- Han, P. and Wang, L. (2013). Estimation with Missing Data: Beyond Double Robustness. Biometrika, 100 (2), 417-430.